A Novel Quantum Visual Secret Sharing Scheme

Inspired by Naor et al.'s visual secret sharing (VSS) scheme, a novel n out of n quantum visual secret sharing (QVSS) scheme is proposed, which consists of two phases: sharing process and recovering process. In the first process, the color information of each pixel from the original secret image is encoded into an n-qubit superposition state by using the strategy of quantum expansion instead of classical pixel expansion, and then these n qubits are distributed as shares to n participants, respectively. During the recovering process, all participants cooperate to collect these n shares of each pixel together, then perform the corresponding measurement on them, and execute the n-qubit XOR operation to recover each pixel of the secret image. The proposed scheme has the advantage of single-pixel parallel processing that is not available in the existing analogous quantum schemes and perfectly solves the problem that in the classic VSS schemes the recovered image has the loss in resolution. Moreover, its experiment implementation with the IBM Q is conducted to demonstrate the practical feasibility.Comment: 19 pages, 13 figure

Mycobacteria Infection in Incomplete Transverse Myelitis Is Refractory to Steroids: A Pilot Study

Incomplete transverse myelitis (ITM) of unknown origin is associated with high rates of morbidity and mortality. This prospective, open-label study was undertaken to determine whether antituberculous treatment (ATT) might help patients with ITM whose condition continues to deteriorate despite receiving IV methylprednisolone treatment. The study consisted of 67 patients with steroid-refractory ITM in whom Mycobacterium tuberculosis (MTB) was suspected clinically and in whom other known causes of myelopathy were excluded. The study occurred from January 2003 to June 2010. Patients underwent trial chemotherapy with ATT. Efficacy was assessed by the American Spinal Injury Association (ASIA) scoring system, the Barthel Index (BI) and the Hauser Ambulation Index (AI) at baseline, 12 months, and 24 months, using magnetic resonance imaging (MRI). Of the 67 patients enrolled, 51 were assessed and 16 withdrew. At 24 months, 49 patients experienced benefits as indicated by significantly increased ASIA and BI scores. The Hauser AI index also improved with markedly decreased abnormal signals in spinal cord MRI over time. The results from this prospective study provide beneficial clinical and MRI data on the efficacy of ATT in ITM patients and suggests mycobacteria may be an important and neglected cause of myelitis

Genomic analyses provide insights into peach local adaptation and responses to climate change

The environment has constantly shaped plant genomes, but the genetic bases underlying how plants adapt to environmental influences remain largely unknown. We constructed a high-density genomic variation map of 263 geographically representative peach landraces and wild relatives. A combination of whole-genome selection scans and genome-wide environmental association studies (GWEAS) was performed to reveal the genomic bases of peach adaptation to diverse climates. A total of 2092 selective sweeps that underlie local adaptation to both mild and extreme climates were identified, including 339 sweeps conferring genomic pattern of adaptation to high altitudes. Using genome-wide environmental association studies (GWEAS), a total of 2755 genomic loci strongly associated with 51 specific environmental variables were detected. The molecular mechanism underlying adaptive evolution of high drought, strong UVB, cold hardiness, sugar content, flesh color, and bloom date were revealed. Finally, based on 30 yr of observation, a candidate gene associated with bloom date advance, representing peach responses to global warming, was identified. Collectively, our study provides insights into molecular bases of how environments have shaped peach genomes by natural selection and adds candidate genes for future studies on evolutionary genetics, adaptation to climate changes, and breeding.info:eu-repo/semantics/publishedVersio

SRSF1 modulates PTPMT1 alternative splicing to regulate lung cancer cell radioresistance

Background Radioresistance is the major cause of cancer treatment failure. Additionally, splicing dysregulation plays critical roles in tumorigenesis. However, the involvement of alternative splicing in resistance of cancer cells to radiotherapy remains elusive. We sought to investigate the key role of the splicing factor SRSF1 in the radioresistance in lung cancer. Methods Lung cancer cell lines, xenograft mice models, and RNA-seq were employed to study the detailed mechanisms of SRSF1 in lung cancer radioresistance. Clinical tumor tissues and TCGA dataset were utilized to determine the expression levels of distinct SRSF1-regulated splicing isoforms. KM-plotter was applied to analyze the survival of cancer patients with various levels of SRSF1-regulated splicing isoforms. Findings Splicing factors were screened to identify their roles in radioresistance, and SRSF1 was found to be involved in radioresistance in cancer cells. The level of SRSF1 is elevated in irradiation treated lung cancer cells, whereas knockdown of SRSF1 sensitizes cancer cells to irradiation. Mechanistically, SRSF1 modulates various cancer-related splicing events, particularly the splicing of PTPMT1, a PTEN-like mitochondrial phosphatase. Reduced SRSF1 favors the production of short isoforms of PTPMT1 upon irradiation, which in turn promotes phosphorylation of AMPK, thereby inducing DNA double-strand break to sensitize cancer cells to irradiation. Additionally, the level of the short isoform of PTPMT1 is decreased in cancer samples, which is correlated to cancer patients' survival. Conclusions Our study provides mechanistic analyses of aberrant splicing in radioresistance in lung cancer cells, and establishes SRSF1 as a potential therapeutic target for sensitization of patients to radiotherapy

Financing Constraints and Stock Price Volatility Empirical Evidence from Shanghai and Shenzhen A-share listed Companies

Based on the theoretical analysis of financing constraints and stock price volatility, the hypothesis of “corporate financing constraints inhibiting corporate stock price volatility” is proposed. After data cleaning, the cross-sectional data based on A-share was used to make an empirical analysis of the relationship between financing constraints and stock price volatility of listed companies in 2018 through regression model. The study found that when companies relax financing constraints, due to widespread overinvestment, the stock price of companies will fluctuate more. In addition, we have shown that by replacing the return of financing constraint indicators and the regression of subsamples based on enterprise size, market type and ownership, the conclusion of the study is more robust. The research reveals the mechanism of the impact of financing constraints on the volatility of corporate stock prices. The conclusions have practical significance for investors, corporations and relevant regulatory authorities

Effect of heat treatment on mechanical and biodegradable properties of an extruded ZK60 alloy

ZK60 magnesium alloy possess good mechanical properties and is a potential biodegradable material. But its high degradation rate is not desirable. In this study the effect of heat treatment on the biodegradable property of ZK60 alloy was investigated. T5 treated, T6 treated, as-cast and as-extruded ZK60 alloys were studied. Microstructure characterization, electrochemical measurement and immersion test were carried out. The results showed that both the mechanical properties and degradation behavior were improved after T5 treatment due to the formation of small and uniformly distributed MgZn phases. The as-cast alloys also exhibited good corrosion resistance. However, the as-extruded and T6 treated samples were severely corroded due to the formation of large amounts of second phases accelerating the corrosion rate owing to the galvanic corrosion. The corrosion resistance of ZK60 alloy was as following: T5 treated > as-cast > T6 treated > as-extruded

Research on Oil Well Production Prediction Based on Radial Basis Function Network

Selection of well and reservoir is an important step in the process of stimulation and transformation of oil fields. Good measures can effectively save the cost in the process of oil field development and greatly increase the production of oil fields. Aiming at the problem of well and reservoir selection in petroleum engineering, a method of oil well production prediction based on radial basis function network is proposed in this paper. According to the field data of Xinjiang oilfield, the main controlling factors with greater influence are selected by correlation analysis after data pretreatment. Then we randomly divide the data into training data set and prediction data set, and use the training data set to create a radial basis function network. Finally, we use the radial basis function network to predict the prediction data set, and the final prediction accuracy reaches 80%